Algoritmos genéticos aplicados na escolha da taxa de amostragem em identificação de sistemas

• Ano de defesa: 2016
• Autor(a) principal: Fagundes, Luis Paulo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Identificação de sistemas não lineares; Algoritmos genéticos; Célula combustível; NARMAX; Sistemas não lineares; Nonlinear system identification; Genetic algorithm; Fuel cell; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14628; http://doi.org/10.14393/ufu.di.2016.271

The present work has as the main goal to introduce a new method to select the sample time of input and output signals used in the identification process using NARMAX representation. To achieve this goal is proposed a genetic algorithm wich uses a supersampled signal, i.e., a signal sampled in the most high frequency available, and later decimation rates are used to create different individuals from the high frequency sample signal. The individuals evaluation uses a system identification with NARMAX representation. The evaluation of the proposed method used a genetic algorithm developed in the software Matlab®. The proposed method was applied in the process identification of a polimeric membrane fuel cell temperature model and the results are presented.